Packt+ | Advance your knowledge in tech

0

All Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Practical Data Science Cookbook, Second Edition

You're reading from Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

Product type Paperback

Published in Jun 2017

Publisher Packt

ISBN-13 9781787129627

Length 434 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Analysis

Authors (5):

Tattar

Bhushan Purushottam Joshi

Sean P Murphy

ABHIJIT DASGUPTA

Anthony Ojeda

+1 more

View More author details

Table of Contents (17) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Preface

1. Preparing Your Data Science Environment FREE CHAPTER

2. Driving Visual Analysis with Automobile Data with R

3. Creating Application-Oriented Analyses Using Tax Data and Python

4. Modeling Stock Market Data

5. Visually Exploring Employment Data

6. Driving Visual Analyses with Automobile Data

7. Working with Social Graphs

8. Recommending Movies at Scale (Python)

9. Harvesting and Geolocating Twitter Data (Python)

10. Forecasting New Zealand Overseas Visitors

11. German Credit Data Analysis

Exploring where the jobs are, by industry

In the previous recipe, we saw how to visualize the top-level aggregate data on pay. The employment dataset has more granular data, divided by public/private sectors and types of jobs. The types of jobs in this data follow a hierarchical coding system called North American Industry Classification System (NIACS). In this recipe, we will consider four particular industries and look at visualizing the geographical distribution of employment in these industries, restricted to private sector jobs.

We will look at four industrial sectors in this recipe:

Agriculture, forestry, fishing, and hunting (NIACS 11)
Mining, quarrying, and oil and gas extraction (NIACS 21)
Finance and insurance (NIACS 52)
Professional and technical services (NIACS 54)

How to do it...

We need to create a subset of the employment data, including the data for industrial sectors, but restricting it to the private sector, by performing the following steps:

We start by filtering the data by the...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at €14.99/month. Cancel anytime

Authors (5)

Tattar

Tattar

Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & Analytics division of Ford Motor Company, Chennai. He received the IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during his PhD. He has authored books such as Statistical Application Development with R and Python, 2nd Edition, Packt; Practical Data Science Cookbook, 2nd Edition, Packt; and A Course in Statistics with R, Wiley. He has created many R packages.

See other products by Tattar

Bhushan Purushottam Joshi

Bhushan Purushottam Joshi

Bhushan Purushottam Joshi is a teacher of computer science and has around 11 years of experience in teaching. He started his career as a programmer in a software firm but found true joy in teaching. He is a teacher by choice and not by chance. He teaches computer science courses such as MCA, MSc IT, BSc IT, and BSc CS at various colleges in Mumbai. He is a master at presenting technical as well as conceptual subjects in the most simplified manner. He has exemplary skills in relating daily life examples to technical concepts, which facilitates understanding of the subject matter. He enjoys teaching technical as well as conceptual subjects such as web design, Java, C#, C++, operating systems, computer networks, data structures, and ethical hacking. He is quite popular and appreciated among his students for his able guidance in their project work

See other products by Bhushan Purushottam Joshi

Sean P Murphy

Sean P Murphy

Sean Patrick Murphy spent 15 years as a senior scientist at The Johns Hopkins University, Applied Physics Laboratory, where he focused on machine learning, modeling and simulation, signal processing, and high performance computing in the Cloud. Now, he acts as an advisor and data consultant for companies in San Francisco, New York, and Washington DC. He completed graduation from The Johns Hopkins University and got his MBA from the University of Oxford. He currently co-organizes the Data Innovation DC meetup and co-founded the Data Science MD meetup. He is also a board member and cofounder of Data Community DC.

See other products by Sean P Murphy

ABHIJIT DASGUPTA

ABHIJIT DASGUPTA

<p>Abhijit Dasgupta is a data consultant working in the greater DC-Maryland-Virginia area, with several years of experience in biomedical consulting, business analytics, bioinformatics, and bioengineering consulting. He has a PhD in biostatistics from the University of Washington and over 40 collaborative peer-reviewed manuscripts, with strong interests in bridging the statistics/machine-learning divide. He is always on the lookout for interesting and challenging projects, and is an enthusiastic speaker and discussant on new and better ways to look at and analyze data. He is a member of Data Community DC and a founding member and co-organizer of Statistical Programming DC (formerly R Users DC)</p>

See other products by ABHIJIT DASGUPTA

Anthony Ojeda

Anthony Ojeda

Tony Ojeda is an accomplished data scientist and entrepreneur, with expertise in business process optimization and over a decade of experience creating and implementing innovative data products and solutions. He has a master's degree in finance from Florida International University and an MBA with a focus on strategy and entrepreneurship from DePaul University. He is the founder of District Data Labs, is a cofounder of Data Community DC, and is actively involved in promoting data science education through both organizations.

See other products by Anthony Ojeda

Other recommended products

Related to this chapter

Hands-On Ensemble Learning with R

Hands-On Ensemble Learning with R

This book introduces you to the concept of ensemble learning and demonstrates how different machine learning algorithms can be combined to build efficient machine learning models. Use R to implement the popular trilogy of ensemble techniques, i.e. bagging, random forest and boosting, to build faster and more accurate machine learning models.

Jul 2018 12h 32m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Hands-On Time Series Analysis with R

Hands-On Time Series Analysis with R

This book introduces you to time series analysis and forecasting with R; this is one of the key fields in statistical programming and includes techniques for analyzing data to extract meaningful insights. You will explore methods, such as prediction with time series analysis, and identify the relationship between each data point in the series.

May 2019 14h 56m

Network Science with Python and NetworkX Quick Start Guide

Network Science with Python and NetworkX Quick Start Guide

The emerging field of Network Science is about understanding different kind of relationships. This book covers the latest version 2.x of NetworkX for performing Network Science with Python.You will also learn the fundamentals of network theory and see practical examples of how they are applied to real-world problems using Python and NetworkX.

Apr 2019 6h 20m

R Programming Fundamentals

R Programming Fundamentals

Data analysis is crucial to accurately predict the performance of an application. The book begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you can read ahead and learn all about data visualization and graphics. You can learn data management, statistics and applications, forecasting, and reporting. With this various case studies and examples, this book gives you the knowledge to confidently start your career in the field of data science.

Sep 2018 6h 52m

Jupyter for Data Science

Jupyter for Data Science

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations and visualizations. This book will be a comprehensive guide to getting started with data science using the popular Jupyter notebook. It will show you how to leverage the capabilties of Jupyter to perform various data science tasks efficiently. From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter.

Getting Started with Haskell Data Analysis

Getting Started with Haskell Data Analysis

Data analysis is a part of computer science and part statistics. An important part of data analysis is validating your assumptions with real-world data to see if there is a pattern or a particular user behavior that you can validate. This book will help you get up to speed with the basics of data analysis and approaches in the Haskell language.

Oct 2018 5h 20m

R Data Analysis Cookbook

R Data Analysis Cookbook

Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book empowers you by showing you ways to use R to generate professional analysis reports. The book also teaches you to quickly adapt the example code for your own needs and save yourself the time needed to construct code from scratch.

Sep 2017 18h 40m

Practical Time Series Analysis

Practical Time Series Analysis

Practical Time Series Analysis will introduce you to the basic concepts of time series analysis and describe powerful yet simple techniques in Python which data scientists and data engineers would find useful in dealing with real life datasets in industrial settings. This book focuses on explaining important concepts and practical techniques to process, summarize and model time series data. Real life case studies with code snippets in Python are used to demonstrate the concepts and techniques.

Machine Learning with R Cookbook

Machine Learning with R Cookbook

The R language is a powerful open source functional programming language. At its core, R is a statistical language that provides impressive tools to analyze data and create high-level graphics. This book covers the basics of R by setting up a user-friendly programming environment and programming ETL in R. Data exploration examples are provided that demonstrate how powerful data visualisation and machine learning is in discovering hidden relationships. You will also explore air quality data, steps to fix the missing values and visualising the same. You will then dive into important machine learning topics, including data classification, regression, survival analysis, time series analysis, clustering association rule mining, and dimension reduction.This book will include the latest code and examples based on R 3.3 and above—updated for better computation, accuracy, and speed with R.

Oct 2017 19h 4m

Mastering Machine Learning with R

Mastering Machine Learning with R

Machine learning is the field of Artificial Intelligence where we build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2.

Apr 2017 14h 0m

Data Analysis with R

Data Analysis with R

R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.

Mar 2018 19h 0m

Personalised recommendations for you

Based on your interests and search pattern

Mathematics of Machine Learning

Mathematics of Machine Learning

Deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems with structured guidance. Gain the confidence to engage with advanced ML literature and tailor algorithms to meet your project requirements.

May 2025 24h 20m

Generative AI with Python and PyTorch

Generative AI with Python and PyTorch

Learn how to create images and text using VAEs, GANs, LSTMs, and transformers. Implement applications in natural language processing and computer vision through practical tutorials.

Mar 2025 15h 0m

Practical Generative AI with ChatGPT

Practical Generative AI with ChatGPT

This book helps you unlock ChatGPT's potential to make your working life better. From prompt engineering to creating custom GPTs, you'll enhance your productivity, creativity, and efficiency with practical insights and advanced techniques.

Apr 2025 12h 52m

Generative AI with LangChain

Generative AI with LangChain

Gain a solid foundation in LangChain, agentic AI, and LangGraph, and learn to build production-ready systems with multi-agent architectures, advanced RAG pipelines, Tree of Thought reasoning, agent handoffs, and fine-grained error handling.

May 2025 15h 52m

Architecting Power BI Solutions in Microsoft Fabric

Architecting Power BI Solutions in Microsoft Fabric

Power BI provides several options to solve common data problems, and designing the correct solution for each scenario can be a daunting task. This book makes it easier by guiding you through designing optimal solutions using Power BI.

Apr 2025 14h 16m

Microsoft Identity and Access Administrator SC-300 Exam Guide

Microsoft Identity and Access Administrator SC-300 Exam Guide

This comprehensive guide covers key topics such as Microsoft Entra ID implementation, authentication and access management, external user management, and hybrid identity solutions, providing practical insights and techniques for SC-300 exam success.

Mar 2025 19h 48m

LLM Design Patterns

LLM Design Patterns

This book helps you gain practical skills to develop and deploy LLMs. You'll learn data prep, training, pruning, quantization, and evaluation, as well as explore RAG, advanced prompting, and optimization to build robust, scalable language models.

May 2025 17h 48m

Tableau Cookbook for Experienced Professionals

Tableau Cookbook for Experienced Professionals

Advance your Tableau knowledge beyond the basics, streamline dashboard performance, tackle advanced geospatial challenges, and unlock API potential while fortifying your corporate data infrastructure with proven best practices.

Apr 2025 12h 24m

Time Series Analysis with Spark

Time Series Analysis with Spark

This book offers a complete guide to time series analysis with Apache Spark and Databricks, covering essential concepts and advanced techniques including Generative AI to equip readers with skills for real-world challenges across industries.

Mar 2025 9h 56m

Hands-On Artificial Intelligence for IoT

Hands-On Artificial Intelligence for IoT

Transform IoT systems with the power of artificial intelligence using this hands-on guide. Dive into practical techniques and expert insights to innovate and optimize your IoT devices, making them smarter and more efficient.

May 2025 15h 44m